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app.py

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+from fastai import *
+from fastai.vision.all import *
+import gradio as gr 
+import skimage
+learn = load_learner('export.pkl')
+## function to use with gradio 
+## we need this to make prediction  on future images 
+labels = learn.dls.vocab ## retrives labels 
+def predict(img):
+    img = PILImage.create(img) # read images 
+    pred,pred_idx,probs = learn.predict(img) ### get pred , pred_index and prob for a a given image 
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    title = " Car type  Classifier"
+description = "A car classifier trained using <a href='https://www.kaggle.com/datasets/jutrera/stanford-car-dataset-by-classes-folder'> the  Oxford car dataset </a>  with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://aniba' target='_blank'> github code</a></p>"
+examples=["2009_bugatti_veyron_grand_sport_10.jpg", "07-x5-bmw.jpg"]
+interpretation='default'
+enable_queue=True
+gr.Interface(fn=predict, 
+             inputs=gr.inputs.Image(shape=(512, 512)), 
+             outputs=gr.outputs.Label(num_top_classes=3),
+             examples=examples,
+             title=title,
+             description=description,
+             article=article,
+             enable_queue= enable_queue,
+             interpretation=interpretation).launch(share=True)
+
+